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Input feature selection by mutual information based on Parzen window
Abstract
Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.
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Cited by
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García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
A survey on online feature selection with streaming features
Hu, X., Zhou, P., Li, P., Wang, J., & Wu, X. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-016-5489-3 · Cited by 88
Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
Hou, M., Xu, C., Li, Z., Liu, Y., Liu, W., Chen, E., & Bian, J. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512056 · Cited by 59
Metadata
- Authors
- Kwak, N., & Choi, C.-H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-12
- Volume
- 24
- Issue
- 12
- Pages
- 1667-1671
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2134478553
- Cited by
- 641
- References
- 0
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
